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Ali Ghodsi

Research interests

Professor Ghodsi's research interests lie at the interface of statistics and computer science. They span a variety of areas in computational statistics particularly in the areas of machine learning and probabilistic modelling.

Some of Professor Ghodsi's current work focuses on the dimensionality reduction (manifold learning) problem. Dimensionality reduction addresses the problem of dealing with complex data by mapping high-dimensional data into fewer dimensions. Many problems of scientific interest that require the analysis of very large high-dimensional data sets can benefit from dimensionality reduction techniques.

An essential part of the information that is totally ignored by existing dimensionality reduction techniques is knowledge about the sequence of the observations and the actions between data points. Professor Ghodsi recently co-developed a new dimensionality reduction technique called Action Respecting Embedding (ARE) which exploits this additional information, successfully translating actions into meaningful and interpretable low-dimensional representations. This led to novel solutions to sequential decision problems such as planning (i.e., finding a sequence of actions to achieve a particular outcome) and localization (i.e., maintaining a representation of one's location). Unlike existing techniques, this approach requires no expert knowledge about the domain to find effective solutions. Professor Ghodsi is working to refine this new technique and to make it more efficient and scalable. This is a potential solution to a large number of problems of scientific interest, including industrial processes and inventory management.

On the more theoretical side, Professor Ghodsi is exploring and formalizing nonlinear dimensionality reduction techniques as probabilistic models. He is addressing the problem of how such models should be constructed, and how they should respond when data is missing. This has many potential uses in fields such as physics, economics, and medicine, where meaningful information must be extracted from large data sets.

Education/biography

Professor Ghodsi is currently a member of the Centre for Computational Mathematics in Industry and Commerce, and the Artificial Intelligence Research Group at the University of Waterloo. He has worked in two other world-class research environments at the University of Toronto and the University of Alberta.

In particular, over the past three years he has spent a significant amount of research time at the Probabilistic and Statistical Inference Group at the University of Toronto and at the Alberta Ingenuity Centre for Machine Learning at the University of Alberta, where he collaborated on statistical machine-learning methods applied to robotics and pattern recognition problems. Since 1992 he has spent five years in industry where he was involved with both software design and implementation.